Gain insights into predicting retail sales with TensorFlow. Delve into data analysis, training models, and extracting insights using industry techniques. Explore efficient model evaluation with experts from top tech companies.

APLCS.tar.gz

case-study

In this course, you'll work on an industry-level machine learning project based on predicting weekly retail sales given different factors. You will learn the most efficient techniques used to train and evaluate scalable machine learning models. After completing this course, you will be able to take on industry-level machine learning projects, from data analysis to creating efficient models and providing results and insights.

The code for this course is built around the TensorFlow framework, which is one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Basic knowledge of Python and TensorFlow are prerequisites. To get some experience with TensorFlow, try our course: Machine Learning for Software Engineers.

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Applied Machine Learning: Industry Case Study with TensorFlow

# Chapter Goals:
- Create the Example spec that's used to parse serialized Example objects

# A. Creating the Example spec

Since the data is now stored as serialized Example objects in TFRecords files, we need to create an Example spec, which allows us to parse the serialized Examples in the input pipeline. The Example spec gives specifications on each of the dataset's features, specifically the shape and type of the feature's values.

The Example spec is just a Python dictionary, mapping feature names to `FixedLenFeature` objects. For our dataset, each of the `FixedLenFeature` objects has shape `()`. This is because each feature contains a single value per data observation, i.e. each feature of our dataset's Example objects contains one value (as opposed to an array of values).

The type of each `FixedLenFeature` depends on the type of values in the feature. Integer valued features will have `tf.int64` type, float valued features will have `tf.float32` type, and string valued features (in our case, only the `'Type'` feature) will have `tf.string` type.

Create an Example spec used for parsing serialized Examples.

What you'll learn from this course

Preliminary Data Analysis

Data Processing

Model Predictions

Example Spec

Chapter Goals:

A. Creating the Example spec